Lung microbiota composition, respiratory mechanics, and outcomes in COVID-19-related ARDS

ABSTRACT Few data are available on the lung microbiota composition of patients with coronavirus disease 2019-related acute respiratory distress syndrome (C-ARDS) receiving invasive mechanical ventilation (IMV). Moreover, it has never been investigated whether there is a potential correlation between lung microbiota communities and respiratory mechanics. We performed a prospective observational study in two intensive care units of a university hospital in Italy. Lung microbiota was investigated by bacterial 16S rRNA gene sequencing, performed on bronchoalveolar lavage fluid samples withdrawn after intubation. The lung bacterial communities were analyzed after stratification by respiratory system compliance/predicted body weight (Crs) and ventilatory ratio (VR). Weaning from IMV and hospital survival were assessed as secondary outcomes. In 70 C-ARDS patients requiring IMV from 1 April through 31 December 2020, the lung microbiota composition (phylum taxonomic level, permutational multivariate analysis of variance test) significantly differed between who had low Crs vs those with high Crs (P = 0.010), as well as in patients with low VR vs high VR (P = 0.012). As difference-driving taxa, Proteobacteria (P = 0.017) were more dominant and Firmicutes (P = 0.040) were less dominant in low- vs high-Crs patients. Similarly, Proteobacteria were more dominant in low- vs high-VR patients (P = 0.013). After multivariable regression analysis, we further observed lung microbiota diversity as a negative predictor of weaning from IMV and hospital survival (hazard ratio = 3.31; 95% confidence interval, 1.52–7.20, P = 0.048). C-ARDS patients with low Crs/low VR had a Proteobacteria-dominated lung microbiota. Whether patients with a more diverse lung bacterial community may have more chances to be weaned from IMV and discharged alive from the hospital warrants further large-scale investigations. IMPORTANCE Lung microbiota characteristics were demonstrated to predict ventilator-free days and weaning from mechanical ventilation in patients with acute respiratory distress syndrome (ARDS). In this study, we observed that in severe coronavirus disease 2019 patients with ARDS who require invasive mechanical ventilation, lung microbiota characteristics were associated with respiratory mechanics. Specifically, the lung microbiota of patients with low respiratory system compliance and low ventilatory ratio was characterized by Proteobacteria dominance. Moreover, after multivariable regression analysis, we also found an association between patients’ microbiota diversity and a higher possibility of being weaned from mechanical ventilation and discharged alive from the hospital. For these reasons, lung microbiota characterization may help to stratify patient characteristics and orient the delivery of target interventions. (This study has been registered at ClinicalTrials.gov on 17 February 2020 under identifier NCT04271345.) 
Clinical Trial
 Registered at ClinicalTrials.gov, 17 February 2020 (NCT0427135).


KEYWORDS ARDS, COVID-19, mortality, microbiota, respiratory mechanics
A cute respiratory distress syndrome (ARDS) is a common though heterogeneous disease leading to high mortality and costs (1), whose burden has been recently exacerbated by the coronavirus disease 2019 (COVID-19) pandemic (2).The COVID-19related ARDS (C-ARDS) (3) is characterized by physiological abnormalities of typical ARDS (2), which include inflammatory alveolar flooding and interstitial infiltrations leading to reduced respiratory system compliance (Crs) (4), and capillary thrombosis fostering pulmonary dead space increase, which directly correlates with the ventilatory ratio (VR) (5)(6)(7)(8).In critically ill patients with non-COVID-19 (9,10) and COVID-19 (11) ARDS requiring invasive mechanical ventilation (IMV), both of these conditions were reported to predict mortality.Furthermore, recent evidence showed that inflammatory exudate that characterized ARDS lungs acts as a nutrient-enriched microbiological ground that, in conjunction with altered local O 2 and CO 2 tension, influences the growth of certain microbiota communities, thus triggering additional immune response and injury (6).In critical care practice, lung microbiota composition was demonstrated to correlate with ventilator-free days (12) in a general population of critically ill patients, as well as it predicts the successful extubation and mortality in those with COVID-19 ARDS (13).
In light of the above considerations, we formulated the hypothesis that different lung microbiota features may correlate with specific respiratory mechanics phenotypes.Accordingly, we prospectively investigated the relationship between the heterogene ity of lung microbiota (diversity and composition) and respiratory system mechanics assessed via Crs and VR in critically ill patients with COVID-19 ARDS.Moreover, as a secondary outcome and proof of concept, we explored potential relationships between lung microbiota composition and relevant clinical outcomes like successful weaning from IMV and hospital survival in those intensive care unit (ICU) patients.

Lung microbiota analysis in C-ARDS patient groups
We compared lung bacterial communities of patients with low Crs vs high Crs and those of patients with low VR vs high VR (eFigures 4-8).In the alfa-diversity (within-sam ple diversity) analysis (eTable 3), communities of low-Crs or low-VR patients did not differ from those of high-Crs or high-VR counterparts when assessed both in terms of community richness (observed species per sample; P = 0.075 and P = 0.075, respectively) and Shannon diversity index (P = 0.256 and P = 0.805, respectively).In the beta-diversity (between-sample diversity) analysis, the principal coordinate analysis (PCoA)-based lung microbiota composition showed that samples from low-Crs patients overlapped with those from high-Crs patients (Fig. 1A).This was also noted for samples from low-VR patients and high-VR patients (Fig. 2A), indicating that the experimental design was appropriate.However, whereas biplot analysis allowed for visualization of dominant (i.e., observed at >0.1%) family taxa across the groups (Fig. 1B  patients than in high-VR patients (P = 0.037).An example of these findings is reported in Fig. 3.

Relationship of lung microbiota composition with C-ARDS laboratory variables
We investigated the relationship of lung microbiota-composing phyla (including Proteobacteria and Firmicutes) with the C-ARDS laboratory variables (D-dimer, LDH, and procalcitonin) (Table 1).We observed a significantly negative correlation between Firmicutes and procalcitonin (P = 0.022) and a positive correlation between Proteobacte ria and procalcitonin (P = 0.024).No correlation was instead observed for Firmicutes or Proteobacteria with D-dimer or LDH, respectively, as well as for Actinobacteria or Bacteroidetes with any of these variables (Fig. 4).

Predictors of C-ARDS-related outcomes
We finally investigated potential predictors of weaning from IMV and hospital survival (Table 2).In addition to demographic, clinical, and respiratory mechanics variables shown in Table

DISCUSSION
We observed that BAL-isolated bacterial communities were associated with Crs and VR.Moreover, we identified one community dominated by Proteobacteria (with the archetypal Acinetobacter) and another dominated by Firmicutes (with the archetypal Lactobacillus or Paenibacillus), regardless of respiratory mechanics alterations.Also, we found that Proteobacteria positively correlated, while Firmicutes negatively, with serum procalcitonin, suggesting that Proteobacteria may contribute to ARDS-related systemic inflammation.Furthermore, patients with greater lung microbiota diversity had a  significantly higher chance of being weaned from IMV and discharged alive from the hospital.
Consistent with the concept that oxygenation and ventilation are physiologically distinct processes ( 5), Crs and VR values allowed the identification of patients with different lung diseases, thus reflecting a different microbiota from healthy human lungs (14).It is likely that physicochemical (oxygen tension or pH) variations of the lung favored the predominance of Proteobacteria and Firmicutes over other species (14).The predicted lower number of aerated lung units in low-Crs patients as compared to high-Crs patients may have stimulated a bloom of facultative anaerobic Proteobacteria (mainly Gammaproteobacteria) (3).These include Moraxellaceae species commonly resident in the human oral cavity (15), but that, whenever dispersed to the lungs (9), may be actively selected by injured alveoli (6).Firmicutes such as Lactobacillaceae species usually maintain an acidic niche in the lung, whereas Proteobacteria-including Acinetobacter or Pseudomonas species-may be hindered from thriving (16).However, the Firmicutes/Proteobacteria ratio can reverse due to an unstable structure of the lung microbiota, which may also be affected by abnormal CO 2 metabolism during C-ARDS.Increased levels of PaCO 2 might explain the lesser dominance of Firmicutes of high-VR patients as compared to low-VR patients.
Although MV is the mainstay of ARDS management (17,18), it may be per se harmful (3).Due to this reason, a lung-protective ventilation strategy has become the standard of care (2) to minimize the occurrence of ventilator-induced lung injury (19).At BAL sampling, patients had been ventilated for at least 2 days, a time during which C-ARDSrelated changes in the lung microbiota may have created favorable conditions for Ventilator-Associated Pneumonia (VAP) development (20).Although extensive clinical overlap among ARDS, pneumonia, and COVID-19 makes it difficult to establish the contribution of VAP to the risk of death in the ICU, it is essential to rely on the prevention of VAP on the early weaning from IMV (21).
The association of Crs and VR with poor outcomes in COVID-19 patients either with (11) or without (22) established ARDS has already been shown.Therefore, we investiga ted whether distinct lung microbiota features could have been associated with the risk of not being weaned from IMV and death.After controlling for potential predictors of death, including VAP, we stratified the outcome by the Shannon diversity index, which represents the most accurate way to measure species diversity in the lung bacterial community (20).In one study on BAL fluid samples from 91 patients (17 with and 74 without ARDS) (12), the overall lung community composition (which was enriched of gut-associated taxa such as Lachnospiraceae and Enterobacteriaceae) (23), but not the Shannon diversity index was significantly predictive of worse ICU outcomes (i.e., extubation and 28-day survival).Other indices of lung bacterial diversity were not  significantly predictive of ICU outcomes as in our study, whereas the discrepancy in the Shannon diversity index between that study (12) and ours could be due to the inherently different patient cohorts studied, with our cohort including only ARDS patients.This relative homogeneity in our patient population provided the right background from which differences in the lung microbiota structure could be determined.Unfortunately, we did not provide support for the hypothesis of lung bacterial taxa abundance contri buting to clinical outcomes in C-ARDS patients.To make consistent C-ARDS lung microbiota findings, putative contaminant 16S RNA gene sequences in BAL fluid samples were removed from the analysis.This was consistent with previously developed strategies to limit the negative impact of DNA contamination that arises especially when low microbial biomass samples are analyzed (24).Unsurprisingly, three clusters of taxa, such as pharyngeal-associated taxa, inflammation-associated taxa, or contamination-associated taxa, were identified in the lung bacterial community of healthy lung transplant recipient patients (25).Similarly, the removal of contaminant DNA sequences allowed us to virtually enrich BAL fluid samples with taxa that originated through oral-cavity immigration (i.e., Firmicutes), gut-lung translocation (i.e., Proteobacteria), or aspiration of an altered pharyngeal microbiota.
Finally, it is noteworthy that non-COVID-19 ARDS may present similar aspects in terms of dysbiosis evidence (low alpha diversity and low relative abundance of "protective" oral origin commensal bacteria), especially for the phenotypes with reduced Crs rather than increased VR (12,23,26).Indeed, in both animal and human studies before the COVID-19 pandemic, an enrichment of the lung microbiome with gut bacteria and a decrease in alpha diversity were associated with the occurrence of ARDS, either primary or sepsisinduced (6).Conversely, Kullberg and coworkers found that, in COVID-19 patients, the lung microbiota community composition, linked to total bacterial burden rather than to a-and b-diversity, was associated with successful extubation (13).Unfortunately, we did not investigate the bacterial burden of our patients, but in light of the observed results, we can conclude that both papers support the role of lung microbiota as a driver of non-resolving ARDS and of the complex heterogeneity of such disease.
Our study has some strengths.First, it is one of the few studies investigating the clinical implications of lung microbiota composition in a relatively large number of patients.Second, this is the first study investigating the relationship between lung microbiota features and respiratory mechanics in patients with C-ARDS.However, this study has some limitations.First, we have only focused on single Crs or VR measure ments, not considering the clinical evolution at different time points.The ICU admission before intubation was longer for some patients, which may lead to a potential bias associated with different C-ARDS stages and respiratory mechanics evaluation.Second, we assumed that the lung microbiota characteristics of our patients were typical of C-ARDS, as we did not include ARDS patients without COVID-19.Third, we correlated lung microbiota characteristics with classical biomarkers of cell damage, micro-throm bosis, or infection, whereas no potential relationships with other markers of inflammation were explored.In addition, our lung microbiota characterization targeted only the bacterial component, so we cannot draw any conclusion regarding the role of residual viral replication as a contributing factor to the observed VR/Crs differences and final outcome.While this should improve the comparability of our findings with those of most published studies, we ignored viral or fungal components of the resident microbial community in the lungs.Appreciating these components would have allowed to obtain important information about the abundances (and their clinical significance) of SARS-CoV-2 or other viruses as well as those of Candida or Aspergillus organisms in the lung microbiota of our patients.Future studies are needed to fill the inevitable gap in such a complex and intricate subject of investigation.

Conclusions
Lung bacterial communities of mechanically ventilated C-ARDS patients differed based on the dominance of bacterial taxa, and the observed differences were related to patients' respiratory mechanics.C-ARDS patients with low Crs/low VR had a Proteobacte ria-dominated lung microbiota; conversely, Firmicutes were dominant in the high Crs phenotype.Furthermore, loss in microbiota diversity appeared to be associated with a longer duration of IMV and higher mortality.Early patient stratification driven by microbiota diversity may help to deliver target intervention, thus aiming to optimize the management C-ARDS patients.reads) corresponding to 447 taxa, whereas the sequences' data set was normalized to 49,791 reads per sample.Alpha diversity (i.e., the within-sample diversity) was measured using the observed species and Shannon diversity index, whereas beta diversity (i.e., the between-sample diversity) by the Bray-Curtis distance and visualized by PCoA.Statistical differences between groups were identified using the Kruskal-Wallis test for alpha diversity or the PERMANOVA for beta diversity.Relative abundances were calculated at any taxonomic level (e.g., phylum), and statistical significance between groups was assessed by the Kruskal-Wallis test.
Clinical data analysis was performed using R software v4.2.0 (R Core Team, 2022; Wien, Austria) (Supplemental material).Qualitative data were expressed as absolute and relative percentage frequency, whereas quantitative data were expressed as either mean and SD or median and interquartile range (IQR).Gaussian distribution was previously assessed by the Shapiro-Wilk's test.Differences between groups were assessed by χ 2 test or Fisher-Freeman-Halton's exact test for qualitative data, whereas either Student's t test or Mann-Whitney U test was applied to quantitative data, as appropriate.Correlation analysis was performed by Spearman's rank correlation test.Both uni-and multivariable Cox regression models were fitted to assess predictors of successful weaning from IMV and hospital survival (composite outcome).HRs and 95% CIs were reported.KM survival analysis was applied to assess potential differences in the composite outcome between specific groups.Log-rank P and KM curve were further performed.Due to the complete exploratory nature of the study, we could not calculate a sample size, but for the multivariable Cox regression model, we followed the rule to include 1 predictor every 10 events.For the whole analyses, statistical significance was set at a P value <0.05.

FIG 1
FIG 1 Lung microbiota composition in "low-Crs" and "high-Crs" patients.(A) Low-Crs patients (shown as red-colored circles and ellipse) had a bacterial community composition that differed from that of high-Crs patients (shown as blue-colored circles and ellipse), as assessed by permutational multivariate analysis of variance and visualized by principal coordinate analysis.(B) Biplot analysis showed the family-level bacterial taxa underlying the differences.(C) Rank abundance analysis of dominant family taxa revealed that the lung microbiota of low-Crs patients was enriched of Moraxellaceae and Lactobacillaceae, whereas that of (D) high-Crs patients was enriched with Paenibacillaceae, as assessed by the Kruskall-Wallis test.

FIG 2
FIG 2 Lung microbiota composition in "low-VR" and "high-VR" patients.(A) Low-VR patients (shown as red-colored circles and ellipse) had a bacterial community composition that differed from that of high-VR patients (shown as blue-colored circles and ellipse), as assessed by permutational multivariate analysis of variance and visualized by principal coordinate analysis.(B) Biplot analysis showed the family-level bacterial taxa underlying the differences.(C) Rank abundance analysis of dominant family taxa revealed that Paenibacillaceae were less dominant in low-VR patients than in (D) high-VR patients, as assessed by the Kruskall-Wallis test.

FIG 3
FIG 3 Graphic representation of respiratory mechanics, aerated tissue, and lung microbiota composition in two patients.Computed tomography scan images of lungs from two representative patients with (A) high Crs/PBW (0.61 mL/cmH 2 O/kg) and VR (3.64) values or (B) low Crs/PBW (0.43 mL/cmH 2 O/kg) and VR (1.65) values depicted the distribution of aerated lung tissue, which was detailed in the respective wave frequency graphs.For each patient, the bubble chart shows dominant families in the lung bacterial community

FIG 4
FIG 4 Correlation between serum procalcitonin and lung microbiota composition.Relationship assessment between lung microbiota-composing phyla and C-ARDS laboratory variables by the use of Spearman's rank test showed the absence of correlation of Actinobacteria and Bacteroidetes with the (A) D-dimer, (B) LDH, or (C) PCT serum level.Firmicutes and Proteobacteria were, respectively, found to correlate negatively or positively only with the (C) PCT serum level.

FIG 5
FIG5 Cumulative incidence of the primary outcome according to the Shannon diversity index.Kaplan-Meier curve analysis showed that patients with a high Shannon diversity index value (solid blue line) differed significantly from patients (dotted blue line) with a low Shannon diversity value regarding the median time from BAL sampling to being weaned from mechanical ventilation and alive (8 and 25 days, respectively, log-rank P = 0.0016).

TABLE 1
Characteristics of COVID-19 patients who received mechanical ventilation for ARDS in the ICU, both overall and stratified compliance and VR subgroups (n = 70

TABLE 1
Characteristics of COVID-19 patients who received mechanical ventilation for ARDS in the ICU, both overall and stratified compliance and VR subgroups (n = 70) a

Compliance of respiratory system/PBW c VR c Variables b Total (n = 70) Low (n = 37) High (n = 33) P Low (n = 36) High (n = 34)
a Qualitative data are expressed as absolute and relative percentage frequency, and P values were computed by Fisher-Freeman-Halton's exact test.Quantitative data are instead described as either mean and SD or median and IQR, as appropriate.Between-group comparisons were assessed by either Student's t test or the Wilcoxon-Mann-Whitney U test (Wilcoxon-Pratt signed rank test in the presence of ties).P values on survival and weaning data were computed instead by log-rank test.All variables, except for outcomes, were assessed at the time patients underwent bronchoalveolar fluid sampling.Significant findings are indicated in bold (P < 0.05), and the suggestive ones are in italic (0.05 ≤ P < 0.10).b COPD, chronic obstructive pulmonary disease; PBW, predicted body weight; SAPS, simplified acute physiology score; MV, mechanical ventilation; CRP, C-reactive protein; PaO 2 /FiO 2 , partial pressure of arterial oxygen/fraction of inspired oxygen; PaCO 2 , arterial partial pressure of carbon dioxide; PEEP, positive end-expiratory pressure; P PLAT , plateau pressure; ELrs, elastance of the respiratory system.

TABLE 2
Cox proportional hazards analysis to identify predictors of in-hospital composite outcome for COVID-19 ARDS patients a (Continued on next page)

TABLE 2
Cox proportional hazards analysis to identify predictors of in-hospital composite outcome for COVID-19 ARDS patients a (Continued) Data are expressed as absolute and relative percentage frequency for qualitative variables, while either mean and SD or median and interquartile range (IQR) were applied to quantitative variables, as appropriate.Bold indicates statistical significance, which was set at a P < 0.05.Only statistically significant variables in the univariable analysis were entered into the multivariable model, due to the low number of events per variable.PBW, predicted body weight; SAPS, simplified acute physiology score; MV, mechanical ventilation; C-ARDS, COVID-19-related ARDS; PaO 2 /FiO 2 , partial pressure of arterial oxygen/fraction of inspired oxygen; RR, respiratory rate; PaCO 2 , arterial partial pressure of carbon dioxide; PEEP, positive end-expiratory pressure; P PLAT , plateau pressure; Crs, compliance of the respiratory system; ELrs, elastance of the respiratory system; VR, ventilatory ratio. a